Cover
Vol. 16 No. Special Issue (2020)

Published: June 30, 2020

Pages: 1-12

Conference Article

Outdoor & Indoor Quadrotor Mission

Abstract

The last few years Quadrotor became an important topic, many researches have implemented and tested concerning that topic. Quadrotor also called an unmanned Aerial Vehicle (UAV), it's highly used in many applications like security, civil applications, aid, rescue and a lot of other applications. It’s not a conventional helicopter because of small size, low cost and the ability of vertical and takeoff landing (VTOL). The models kept an eye on quadrotors were presented, the advancement of this new kind of air vehicle is hindered for a very long while because of different reasons, for example, mechanical multifaceted nature, enormous size and weight, and challenges in charge particularly. Just as of late a lot of interests and endeavors have been pulled in on it; a quadrotor has even become a progressively discretionary vehicle for useful application. Quadrotor can be used in variable, different , outdoor and indoor missions; these missions should be implemented with high value of accuracy and quality. In this work two scenarios suggested for different two missions. First mission the quadrotor will be used to reach different goals in the simulated city for different places during one flight using path following algorithm. The second mission will be an indoor arrival mission, during that mission quadrotor will avoid obstacles by using only Pure pursuit algorithm (PPA). To show the benefit of using the new strategy it will compare with a victor field histogram algorithm (VFH) which is used widely in robotics for avoiding obstacles, the comparison will be in terms of reaching time and distance of reaching the goal. The Gazebo Simulator (GS) is used to visualize the movement of the quadrotor. The gazebo has another preferred position it helps to show the motion development of the quadrotor without managing the mathematical model of the quadrotor. The Robotic Operating System (ROS) is used to transfer the data between the MATLAB Simulink program and the Gazebo Simulator. The diversion results show that, the proposed mission techniques win to drive the quarter on the perfect route similarly at the limit with regards to the quadrotor to go without hitting any obstacle in the perfect way.

References

  1. Castillo P., Dzul A. and Lozano R. (2004) Real-time stabilization and tracking of a four-rotor mini rotorcraft. IEEE Trans. Control Syst. Tech. Vol. 12(4), pp.510-516.
  2. Xu R. and Ozguner U. (2008) Sliding mode control of a class of under actuated systems. Automatica. Vol. 44(1), pp. 233-241.
  3. Zuo Z. (2010) Trajectory tracking control design with command-filtered compensation for a quadrotor. IET Control Theory Applied. Vol. 4(11), pp. 2343-2355.
  4. ] Hoffmann G., Rajnarayan D. Waslander S. Dostal D. Jang T. and Tomlin C. 2004 The Stanford testbed of autonomous rotorcraft for multi agent control (STARMAC),'' in Proc. 23rd Digit. Avionics Syst. Conf. pp.
  5. Nils G. Paul B. and Sergio M. 2015 Obstacle Detection and Collision Avoidance for a UAV With Complementary Low-Cost Sensors Digital Object Identifier IEEE Access Vol 3, pp. 599609.
  6. Xin-Zhong P., uei-Yung L. and Jyun-Min D. 2016 Path Planning and Obstacle Avoidance for Vision Guided QuadrotorUAV Navigation 12th IEEE International Conference on Control & Automation.
  7. Prathamesh S. Saee P. Jagdish R. and Arish S. 2014 Quadcopter – Obstacle Detection and Collision Avoidance International Journal of Engineering Trends and Technology (IJETT), Vol 17, No 2, pp. 84-87.
  8. Zhixiang L. Youmin Z. Chi Y. Laurent C. Didier T. 2019 Collision Avoidance and Path Following Control of Unmanned Aerial Vehicle in Hazardous Environment Journal of Intelligent & Robotic Systems Vol 95, Issue 1, pp. 193-210.
  9. Neerendra Kumar, Zoltan Vamossy .- Robot navigation with obstacle avoidance in unknown environment InternationalJournalofEngineering&Technology , 15,Nov,2018
  10. Quiñonez Y., Barrera F., Bugueño I., Bekios-Calfa J. 2018 Simulation and path planning for quadcopter obstacle avoidance in indoor environments using the ROS framework. In: Mejia J., Muñoz M., Rocha Á., Quiñonez Y., Calvo-Manzano J. (eds) Trends and Applications in Software Engineering. CIMPS 2017. Advances in Intelligent Systems and Computing, vol 688. Springer, Cham
  11. Ícaro V. Igor A. Davi S. Luiz G. 2015 Trajectory Tracking Control of an Aerial Robot with Obstacle Avoidance IFAC Vol. 48, Issue 19, pp. 81-86.
  12. Sohan Suvarna , Dibyayan Sengupta , Simulation of Autonomous Airship on ROS-Gazebo Framework “ 2019 Fifth Indian Control Conference (ICC) IIT Delhi, India, January 9-11, 2019 , pp-237-241.
  13. Yao, W., Dai, W., Xiao, J., Lu, H., & Zheng, Z. (2015). A simulation system based on ROS and Gazebo for RoboCup Middle Size League. 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO).
  14. Claudio Sciortino, Adriano Fagiolini, Member, IEEE , ROS/Gazebo-based Simulation of Quadcopter Aircrafts , 978-1-5386-6282-3/18/$31.00 ©2018 IEEE